Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
  • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
  • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
  • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
  • G05B23/0232—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions based on qualitative trend analysis, e.g. system evolution
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/20—Pc systems
  • G05B2219/24—Pc safety
  • G05B2219/24058—Remote testing, monitoring independent from normal control by pc
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/20—Pc systems
  • G05B2219/24—Pc safety
  • G05B2219/24097—Camera monitors controlled machine

Definitions

• the embodiments herein generally relate to automation of analytical diagnostic tool, and more particularly relate to a system and method for determining the health condition and anomalies of an equipment for carrying out electrical and fire safety audits for detecting failures and also to predict possible corrosion in equipment.
• an embodiment herein provides a system for determining the health condition and an anomaly of an equipment.
• the system includes (A) a plurality of portable sensors that are attached to the equipment, wherein the plurality of sensors senses information associated with a plurality of parameters of the equipment, (B) a field device that is communicatively connected to the plurality of sensors, wherein the plurality of sensors is configured to wirelessly communicate the sensor data to the field device, (C) a camera unit that captures visual data associated with the equipment being analyzed, wherein the camera unit is configured to wirelessly communicate the captured images to the field device and (D) a server that is communicatively connected to the field device for receiving the sensor data and the visual data associated with the equipment.
• the server comprises of (a) a database that stores (i) the sensor data and (ii) the visual data, wherein the sensor data comprises values of the plurality of parameters associated with the equipment, and wherein the visual data comprises at least one of (i) a plurality of images of the equipment or (ii) a plurality of videos of the equipment; and (b) a processor that executes a set of modules, wherein the set of modules comprises (i) a fault detection module that processes the sensor data to determine a fault or the health condition of the equipment, (ii) an image processing module that is trained to detect the anomaly in the equipment by processing the visual data associated with the equipment, and (iii) a report generation module that automatically generates a health report based on the health condition of the equipment and the anomaly detected in the equipment.
• the processor comprises a work order module that generates a work order when initiated by a client device, wherein the work order comprises a request to determine a health condition of the equipment and an anomaly in the equipment.
• the sensor data and the visual data of the equipment are tagged with at least one of (i) a customer identifier, (ii) a work order identifier, (iii) a location identifier, (iv) a facility identifier, (v) a floor identifier, (vi) an equipment type, (vii) an anomaly type or (vii) an image identifier or a video identifier.
• the fault detection module communicates with the sensor data and the image data stored in the database and makes use of machine learning module to determine an anomaly if there is a fault.
• the machine learning module may get trained periodically by image processing module that analyses the visual data and determines the type of fault accordingly based on historical data available.
• the health report comprises at least one of (i) a location of the equipment, (ii) an observation based on the sensor data and the visual data, (iii) a recommendation to rectify a fault or an anomaly detected in the equipment, (iv) a criticality level of the fault or the anomaly detected in the equipment or (v) a reference to industry standards and wherein the health report is communicated to the client device.
• the sensor data and (ii) the visual data are communicated from the field device to the server in a plurality of stages, wherein after each of the plurality of stages, the server generates an alert to the field device, wherein the alert comprises data regarding consistency and relevance of (i) the sensor data and (ii) the visual data.
• a method for determining the health condition and an anomaly of equipment comprises the steps of (i) sensing, using a plurality of sensors, information associated with a plurality of parameters of the equipment, wherein the plurality of sensors is attached to the equipment; (ii) communicating, using the plurality of sensors, the sensor data to a field device, wherein the field device is communicatively connected to the plurality of sensors; (iii) capturing, a camera unit, visual data associated with the equipment being analyzed, wherein the camera unit is configured to wirelessly communicate the captured images to the field device; (iv) generating a database, in a server, with (i) the sensor data and (ii) the visual data, wherein the sensor data comprises values of the plurality of parameters associated with the equipment, and wherein the visual data comprises at least one of (i) a plurality of images of the equipment or (ii) a plurality of videos of the equipment; (v) processing, using a fault detection module of the server the
• the method comprises tagging (i) the sensor data and
• a location identifier (iii) a location identifier, (iv) a facility identifier, (v) a floor identifier, (vi) an equipment type, (vii) an anomaly type or (vii) an image identifier or a video identifier.
• the method comprises communicating (i) the sensor data and (ii) the visual data in a plurality of stages from the field device, wherein after each of the plurality of stages, the server (112) generates an alert to the field device (110), wherein the alert comprises data regarding consistency and relevance of (i) the sensor data and (ii) the visual data.
• FIG. 1 illustrates a system for determining a health condition and an anomaly of an equipment according to an embodiment herein;
• FIG. 2 illustrates an exploded view of a server of FIG. 1 according to an embodiment herein;
• FIG. 3 A & FIG. 3B are process flow that illustrates a method for determining a health condition and an anomaly of an equipment according to an embodiment herein;
• FIG. 4 is a block diagram that illustrates a system that comprises a machine learning model for determining a health condition or an anomaly of an equipment according to an embodiment herein;
• FIG. 5 illustrates a user interface view of a client device that shows a health report of the equipment according to an embodiment herein;
• FIG. 6A and FIG. 6B are flow diagrams that illustrate a method for determining the health condition and an anomaly of the equipment using the system of FIG. 1 according to an embodiment herein;
• FIG. 7 is an exploded view of the Processor, according to an embodiment herein.
• FIG. 1 illustrates a system for determining a health condition and an anomaly of an equipment according to an embodiment herein.
• the system includes a field equipment 104, a plurality of sensors 106A-N, a camera unit 108, a field device 110, a server 112, a database unit 114, a client device 116.
• a field engineer 102 is interacting with the field device 110.
• the plurality of sensors 106A-N and the camera unit 108 are arranged around the field equipment 104. This plurality of sensors 106 and the camera unit 108 are communicatively connected to the field device 110.
• the plurality of sensors 106A-N senses information associated with a plurality of parameters of the equipment 104.
• the plurality of sensors 106A-N are configured to wirelessly communicate the sensor data to the field device 110.
• the camera unit 108 captures visual data associated with the equipment being analyzed.
• the camera unit 108 is configured to wirelessly communicate the captured images to the field device 110.
• the field device 110 communicates the sensor data (not shown in the figure) and the visual data (not shown in the figure) to the server 112 over a network and stored in the database 114.
• the plurality of sensors 106A-N associated with the equipment 104 may be fixed at specific points in a physical space near the equipment 104.
• the plurality of sensors 106A-N measures data specific to the equipment 104 they are associated with.
• the data being collected may include a temperature of the equipment 104, a voltage and a current, etc.
• the camera unit 108 may be used to capture photographs of an equipment 104 from different angles. In some embodiments, this camera unit 108 may be used for live streaming of video and audio of the equipment 104 when required.
• the field engineer 102 interacts with the field device 110 by providing instructions to start capturing the data and the data is then transferred to the server 112 by providing a synchronize instruction.
• the field device 110 may have a storage memory in regards to storing the sensor data and the image/visual data within itself until the data is transmitted to the server 112.
• the field device 110 may transmit the sensor data and the image/visual data over the network in a plurality of stages to the server 112.
• the server 112 may generate an alert to the field device 110 regarding the consistency and relevance of the sensed data.
• the sensor data comprises values of the plurality of parameters associated with the equipment 104.
• the visual data comprises at least one of (i) a plurality of images of the equipment or (ii) a plurality of videos of the equipment 104.
• the server 112 processes the sensor data to determine a fault or the health condition of the equipment 104.
• the server 112 is trained to detect an anomaly in the equipment 104 by processing the visual data associated with the equipment 104.
• the server 112 automatically generates a health report based on the health condition of the equipment 104 and the anomaly detected in the equipment 104.
• the server 112 may provide the health report on a client device 116.
• the client 118 may access the health report by interacting with the client device 116.
• FIG. 2 illustrates an exploded view of the server 112 of FIG. 1 according to an embodiment herein.
• the server 112 includes a database 114 where all the visual data and the sensor data is stored after being checked for consistency and relevance, a fault detection module 202, an image processing module 204, a work order module 208 which initiates a work order 210 on initiation from the client device 116, and a report generation module 206.
• the fault detection module 202 processes the sensor data to determine a fault or the health condition of the equipment 104.
• the image processing module 204 is trained to detect an anomaly in the equipment 104 by processing the visual data associated with the equipment 104.
• the report generation module 206 automatically generates a health report 212 based on the health condition of the equipment 104 and the anomaly detected in the equipment 104.
• the health report 212 may be provided as an output on the client device 116.
• the fault detection module 202 and image processing module communicate with the field device 110 to receive the sensor data and the visual data respectively.
• the sensor and visual data may be received in real-time or periodically in at least one stage.
• the server may alert the field device 110 regarding the consistency and relevance of the data received.
• the sensor and visual data may be tagged with at least one of the following (i) a customer identifier, (ii) a work order identifier, (iii) a location identifier, (iv) a facility identifier, (v) a floor identifier, (vi) an equipment type (vii) an anomaly type, (viii) a image identifier or (ix) a video identifier.
• the report generation module 206 may include the facility to generate a health report 212 signed by a customer and to be uploaded when the work order 210 is completed.
• FIG. 3A & FIG. 3B are process flow that illustrates a method for determining a health condition and an anomaly of an equipment 104 according to an embodiment herein.
• a purchase order is received from a customer.
• a unique work order 210 is generated by the work order module 208.
• the work order 210 is approved for execution.
• work/service is allocated to a field engineer 102 from the server 112.
• the field engineer logs in through field service & the service commences.
• images of the equipment 104 are captured & critical observations from the image of the equipment 104 are selected.
• parameters are measured as required by a monitoring system.
• step 316 the sensor and the visual data are captured in field device 110.
• step 318 the sensor and the visual data are transferred to the server 112 in stages or on synchronization.
• step 320 observations from the sensor and the visual data are verified by a senior engineer and modified as required.
• step 324 critical observations from the sensor and the visual data are sent over pre configured mode (SMS/e-mail) to a client device 116.
• step 326 a health report 212 including field readings, images, analysis, observations, recommendations, summary, etc. is generated in a pre-configured format.
• step 326 a request for approval is sent to a manager who supervises the equipment monitoring process.
• step 328 required changes are incorporated by the system as required.
• FIG. 4 is a block diagram that illustrates a system that comprises a machine learning model 410 for determining a health condition or an anomaly of an equipment 104, according to an embodiment herein.
• the system includes a server 112 that employs a machine learning model 410 for determining a health condition or an anomaly of the equipments 104A-N.
• the machine learning model 410 performs analytics over historical data of an equipment 104 and detecting a point of failure for the equipment 104.
• Every equipment 104 includes a unique identifier that identifies a test number for every test on that equipment 104, herein referred to as a tag.
• Data collected with respect to the equipment 104 is marked with the tag of the equipment 104. This way, the structured data is generated and stored in the database 114.
• the data may be photographs of the equipment 104 taken from different angles. The photographs may be combined using the machine learning model 410 in order to get a more complete idea of the equipment’s position in the physical world. This may be helpful in identifying the clearance between a wall near the equipment 104 and the equipment’s panel.
• the machine learning module 410 may be trained with the available historical data to identify defects in the equipment 104 and to predict and detect the faults in the equipment 104. In some embodiments, the machine learning model 410 may be trained to automatically to detect anomaly in the equipment 104.
• the machine learning model 410 identifies a cause that is attributed to an anomaly.
• the causes of anomaly may be attributed to a design of the equipment 104, installation of the equipment 104, maintenance of the equipment 104 and operating conditions of the equipment 104.
• FIG. 5 illustrates a user interface view of a client device that shows a health report 500 of the equipment 104 according to an embodiment herein.
• the health report 500 may include a photograph 502 of the equipment 104.
• the health report 500 has various sections not limiting to basic observations, other observations, summary, etc.
• the health report 500 of the equipment 104 is generated based the data (e.g. the sensor data and the visual data) that is available with respect to the equipment 104 including images, site measurement readings (e.g. the sensor data), observations, recommendations, the criticality of the equipment, etc.
• the health report 500 may comprise at least one of the following details (i) a equipment’s location, (ii) observations on the sensor data, (iii) observations on the visual data, (iv) recommendations to rectify a fault or an anomaly in the equipment 104 or (v) a reference to industry standards.
• the system (as shown in FIG. 1) may automatically populate the above details in the health report 500 at relevant sections without any human intervention.
• the basic structure of the health report 500 is predefined and the equipment specific information is populated in the health report 500 by the system.
• the system may automatically populate other information including customer details, date of field audit activity auditor’s name, make and SI numbers of meter used for measurements, their calibration reports, report engineer’s name and approver’s name, etc. in relevant sections in the health report 500.
• FIG. 6A and FIG. 6B are flow diagrams that illustrate a method for determining the health condition and an anomaly of the equipment 104 using the system of FIG. 1 according to an embodiment herein.
• information associated with a plurality of parameters of the equipment 104 e.g. a sensor data
• the plurality of sensors 106A-N are attached to the equipment 104.
• the sensor data is communicated to the field device 110.
• the field device 110 is communicatively connected to the plurality of sensors 106A-N.
• visual data associated with the equipment 104 is captured using the camera unit 108.
• the camera unit 108 is configured to wirelessly communicate the captured images to the field device 110.
• the database 114 is generated in the server 112 with the sensor data and the visual data.
• the sensor data comprises values of the plurality of parameters associated with the equipment 104 and the visual data comprises at least one of (i) a plurality of images of the equipment 104 or (ii) a plurality of videos of the equipment 104.
• the sensor data is processed using the fault detection module 202 of the server 112 to determine a fault or the health condition of the equipment 104.
• the image processing module 204 of the server 112 is trained to detect an anomaly in the equipment 104 by processing the visual data.
• a report based on the health condition of the equipment and the anomaly detected in the equipment generated using the report generation module 206.
• FIG. 7 is an exploded view of the processor according to an embodiment herein. It is a representative hardware environment for practicing the embodiments herein is depicted in FIG. 7, with reference to FIG. 1 through 6.
• This schematic drawing illustrates a hardware configuration of a server/computer system/ user device in accordance with the embodiments herein.
• the processor 700 includes at least one processing device 10 and a cryptographic processor 11.
• the special-purpose CPU 10 and the cryptographic processor (CP) 11 may be interconnected via system bus 14 to various devices such as a random-access memory (RAM) 15, read-only memory (ROM) 16, and an input/output (I/O) adapter 17.
• RAM random-access memory
• ROM read-only memory
• I/O input/output
• the I/O adapter 17 can connect to peripheral devices, such as disk units 12 and tape drives 13, or other program storage devices that are readable by the system.
• the user device can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein.
• the user device further includes a user interface adapter 20 that connects a keyboard 18, mouse 19, speaker 25, microphone 23, and/or other user interface devices such as a touch screen device (not shown) to the bus 14 to gather user input.
• a communication adapter 21 connects the bus 14 to a data processing network 26, and a display adapter 22 connects the bus 14 to a display device 24, which provides a graphical user interface (GUI) 30 of the output data in accordance with the embodiments herein, or which may be embodied as an output device such as a monitor, printer, or transmitter, for example.
• GUI graphical user interface
• a transceiver 27, a signal comparator 28, and a signal converter 29 may be connected with the bus 14 for processing, transmission, receipt, comparison, and conversion of electric or electronic signals.
• the present system reduces overall human intervention drastically as data collection, analysis and health report generation is automatically performed by the system.
• the present system increases efficiency, accuracy, consistency and unparalleled speed of execution in determining a health condition and an anomaly of the equipment 104.
• the foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments.
• the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the appended claims.

Landscapes

• Physics & Mathematics (AREA)
• Engineering & Computer Science (AREA)
• General Physics & Mathematics (AREA)
• Automation & Control Theory (AREA)
• Artificial Intelligence (AREA)
• Evolutionary Computation (AREA)
• Mathematical Physics (AREA)
• Testing And Monitoring For Control Systems (AREA)

Applications Claiming Priority (2)

Publications (2)

Family

ID=73459169

Family Applications (1)

Country Status (3)

Families Citing this family (3)

Family Cites Families (6)

• 2019
  • 2019-10-25 EP EP19929862.1A patent/EP3973364A4/de active Pending
  • 2019-10-25 US US17/471,030 patent/US11755007B2/en active Active
  • 2019-10-25 WO PCT/IN2019/050786 patent/WO2020234894A1/en unknown

Also Published As

Similar Documents

Legal Events